Epigenetics, the study of non-DNA sequence-related heredity, is at the epicenter of modern medicine because it can help to explain the relationship between an individual's genetic background, the environment, aging, and disease. The Center for the Epigenetics of Common Human Disease was created four years ago to begin to develop the interface between epigenetics and epidemiologic-based phenotype studies, recognizing that epigenetics requires new ways of thinking about disease. We used the reduced funding of the current grant cycle to create a highly interdisciplinary group of faculty and trainees, including molecular biologists, biostatisticians, epidemiologists, and clinical investigators. Working together, we have developed novel approaches to genome-wide DNA methylation (DNAm) analysis, allele-specific expression, and new statistical epigenetic tools. Using these tools, we discovered that most variable DNAm is in neither CpG islands nor proelationship between epigenetic variation, genetic variation, environment and phenotype. We will continue to pioneer genome-wide epigenetic technology that is cost effective for large scale analysis of population-based samples, applying our knowledge from the current period to second-generation sequencing for epigenetic measurement, including DNAm and allele-specific methylation. We will continue to pioneer new statistical approaches for quantitative and binary DNAm assessment in populations, including an Epigenetic Barcode. We will develop Foundational Epigenetic Epidemiology, examining: time-dependence, heritability and environmental relationship of epigenetic marks;heritability in MZ and DZ twins;and develop an epigenetic transmission disequilibrium test. We will then pioneer Etiologic Epigenetic Epidemiology, by integrating novel genome-wide methylation scans (GWMs) with existing Genome-Wide Association Study (GWAS) and epidemiologic phenotype data, a design we term Genome-Wide of disease.

Public Health Relevance

- Most common human disease is caused by a combination of genetic, environmental, and epigenetic factors, the latter of which is non-sequence information that controls gene function and mediates the relationship between genes and the environment. We are creating the new field of epigenetic epidemiology by providing the experimental tools, statistical methods, and approaches to key barriers that have prevented incorporation of epigenetics into research on diseases such as bipolar disorder, autism and age-related illness.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Specialized Center (P50)
Project #
5P50HG003233-10
Application #
8471147
Study Section
Special Emphasis Panel (ZHG1-HGR-N (J1))
Program Officer
Brooks, Lisa
Project Start
2004-05-14
Project End
2014-04-30
Budget Start
2013-05-01
Budget End
2014-04-30
Support Year
10
Fiscal Year
2013
Total Cost
$3,274,024
Indirect Cost
$1,303,099
Name
Johns Hopkins University
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21218
Aryee, Martin J; Jaffe, Andrew E; Corrada-Bravo, Hector et al. (2014) Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics 30:1363-9
Liu, Yun; Li, Xin; Aryee, Martin J et al. (2014) GeMes, clusters of DNA methylation under genetic control, can inform genetic and epigenetic analysis of disease. Am J Hum Genet 94:485-95
Plongthongkum, Nongluk; van Eijk, Kristel R; de Jong, Simone et al. (2014) Characterization of genome-methylome interactions in 22 nuclear pedigrees. PLoS One 9:e99313
Timp, Winston; Feinberg, Andrew P (2013) Cancer as a dysregulated epigenome allowing cellular growth advantage at the expense of the host. Nat Rev Cancer 13:497-510
Liu, Yun; Aryee, Martin J; Padyukov, Leonid et al. (2013) Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat Biotechnol 31:142-7
Jaffe, Andrew E; Murakami, Peter; Lee, Hwajin et al. (2012) Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies. Int J Epidemiol 41:200-9
Jaffe, Andrew E; Feinberg, Andrew P; Irizarry, Rafael A et al. (2012) Significance analysis and statistical dissection of variably methylated regions. Biostatistics 13:166-78
Aryee, Martin J; Wu, Zhijin; Ladd-Acosta, Christine et al. (2011) Accurate genome-scale percentage DNA methylation estimates from microarray data. Biostatistics 12:197-210
Kim, Kitai; Zhao, Rui; Doi, Akiko et al. (2011) Donor cell type can influence the epigenome and differentiation potential of human induced pluripotent stem cells. Nat Biotechnol 29:1117-9
Hansen, Kasper Daniel; Timp, Winston; Bravo, Hector Corrada et al. (2011) Increased methylation variation in epigenetic domains across cancer types. Nat Genet 43:768-75

Showing the most recent 10 out of 29 publications